ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness · HackerPapers discussions

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness — mentioned by waldarbeiter

Mon, 27 May 2024 19:03:25 GMT

For everyone interested in what makes a classifier put an image in a certain class I can recommend the excellent paper by Robert Geirhos et al. "ImageNet-trained CNNs are biased towards texture" [1]. The paper is a bit older but it's an excellent read. [1] https://arxiv.org/pdf/1811.12231

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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness — mentioned by gwern

Wed, 17 Feb 2021 02:37:57 GMT

You can make the CNN focus more on shape rather than photographic texture using style transfer, hence, Stylized-Imagenet: https://arxiv.org/abs/1811.12231 I've wondered if you could do the same thing with anime images as well, and if a joint classifier (using, say, https://www.gwern.net/Danbooru2020 ?) would be much better for it. Of course, the big trend now is CLIP/ALIGN https://openai.com/blog/clip/ https://arxiv.org/abs/2102.05918#google which are trained on web images on such a scale that they do quite fine on anime images. Look on Twitter or Reddit for 'Big Sleep' examples using the…

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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness — mentioned by skybrian

Wed, 11 Sep 2019 21:24:32 GMT

Note that the leading image classification algorithms are apparently trained to recognize texture more than shapes, because that's the easiest way to win at current benchmarks. But that can be fixed: https://arxiv.org/abs/1811.12231

Paper and discussion history. A mention is not an endorsement.